Detection of waste dumping locations in landfill using multi-temporal landsat thermal images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The practice of solid waste disposal in conventional landfills has always been associated with adverse environmental impacts, leading to the migration of landfill gas and bad odour to the proximate areas. Apart from the obnoxious fumes and hazardous leachate, the potential of heat generation within these vast disposal sites has been observed during the aerobic and anaerobic decomposition process. Therefore, this study aims to demonstrate how to utilize thermal remote sensing technique to monitor the heat flux, which can aid in detecting the waste dumping location with a case study in the Jeleeb Al-Shuyoukh landfill in Kuwait, where the record of its physical boundary was found missing. Landsat TM/ETM+ images for ten-year (1985 to 1994) were acquired and subsequently processed with atmospheric correction so as to compute the land surface temperature (LST). Through overlay analysis, the multi-temporal LST contours were combined in order to detect the most probable dumping locations within the landfill. With reference to the 50 borehole locations drilled by the Environmental Public Authority of Kuwait, our results derived during the summer season yielded a better accuracy (72%) comparing to that derived during the winter season (70%). This can be explained by the waste decomposition process reaches to the peak in summer and more heat flux can be captured from the ground cover. In addition, the dumping locations buried with construction waste were found to have higher LST as compared to the sites containing organic waste in most of the cases, except for certain locations which contained the mixture of construction and organic waste in winter season.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it